Breaking Down & Testing FIVE LLM Agent Architectures - (Reflexion, LATs, P&E, ReWOO, LLMCompiler)

preview_player
Показать описание
Large Language Model Agents have taken over LLM and Artificial Intelligence application design by storm, so this time we check out and simplify six main concepts and five popular papers documenting ways to set up language model based agents, as well as directly testing examples.

Resources -

Papers:

LangSmith Traces:

Chapters:
00:00 - Intro
01:08 - Basic Reflection
02:44 - Basic Reflection Testing
06:32 - Reflexion Actor
09:57 - Reflexion Action Testing
12:25 - Language Agent Tree Search (LATs)
17:04 - LATs Testing
20:54 - Plan And Execute
23:38 - Plan And Execute Testing
26:28 - Reasoning Without Observation (ReWOO)
29:26 - ReWOO Testing
31:11 - LLMCompiler
35:19 - LLMCompiler Testing
36:05 - Outro
Рекомендации по теме
Комментарии
Автор

Your channel has been very valuable today to get me situated on how to get the hang of LLM use. I can now start thinking about project ideas to get some practice. Thank you very much !

pinkmatter
Автор

outstanding overview of key the agentic architectures, I learned a ton, prob one of the best out atm - Thanks

MekMoney
Автор

Bravo. I’ve been looking for something like this all week. Now I need to watch your langgraph videos.

Kmysiak
Автор

Thank you for this video. Sometimes it’s hard to see what’s happening in agentic frameworks and this video helps explain what’s going on.

coolmcdude
Автор

This is really great info, thanks a bunch for sharing. What's really eye-opening is the run times and token counts.

kenchang
Автор

That's an amazing work we have here, guys. Cheers to you, bro. Thanks!

PYETech
Автор

Really good break down for folks building, thanks for putting this out

TheFocusedCoder
Автор

Great work, thanks for this🙏. There is another agentic approach which is called self discovery. Would be cool if you cover that as well 😊.

sanesanyo
Автор

I'm impressed with the explanations!

rodfarm
Автор

This breakdown is insanely helpful 👏

I’ve been working as a Web Engineer for > 10 yrs and recently started learning about AI/ML. I began my career as a self-taught dev in the good ol’ jQuery days, but my lack of CS fundamentals is starting to come back an bite me.

These architectural diagrams are incredibly useful for breaking down high-level concepts.

tyler-morrison
Автор

Good overview. It would be very interesting to see the answer quality benchmarks for these techniques. In a lot of real business cases the time and cost have much less importance than the quality.

AlexU-ou
Автор

Very nicely done, thank you for such a good preseentation.

cmthimmaiah
Автор

The volume is super low on this, compared to every other video I have watched today. Consider trying to hit the 0db mark while you speak normally, in your screen capturing app. You don't seem like someone that gets excited or screams, so as long as you just act like yourself, the 0db setting should be perfect for you. Good luck!

ShaunPrince
Автор

Hey! Thanks for clear explanation! Is code available of those agents?

hemigwaypl
Автор

We made a 7th with output focused recursive events at my company :)

Jandodev
Автор

Hi Adam, great work. I've been struggling trying to evaluate the different agent frameworks, autogen, crewai VRSEN and on and on. langchain etc. seems to be more logical as we can see what's happening and is more predictable. Would it be possible to get the Miro you built for this presentation? Greetings from France.

xollob
Автор

Question: If I have a data preprocessing agent that has access to around 20 preprocessing tools, what is the best way to go about executing them on a pandas data frame? Do I have the data frame in the State and then pass that input in the function? Does the agent need to have access to that data frame or can we abstract that?

lavamonkeymc
Автор

¿Puedes compartir con nosotros tu presentación de Miro?, Great Job

ricardoaltamiranomarquez
Автор

Hey can you please share the miro board link? Or drop it into a high res pdf? AWESOME work btw 👍👍👍

JEffigy
Автор

Which would you say is more crucial to analyzing the "correctness" of the language agent tree search result: "blah blah blah" or "yada yada yada"?

genXstream